A Methodological Framework of Human-Machine Co-Evolutionary Intelligence for Decision-Making Support of ATM

Abstract

International audience; Despite of the success of artificial intelligent (AI) methods in many domains, there is big dilemma for AI when applying to air traffic management (ATM). That is AI researchers have long stated their AI methods are effective and reliable enough to handle many ATM problems, while human controllers still refuse to adopt such AI methods. We believe the dilemma is not about whether AI methods is effective or reliable enough, but about why human controllers should be replaced by AI methods. In other words, as long as an AI method aims to compete and replace human controllers, it will be confronted with the difficulty of not being accepted by human controllers. To address this dilemma, this paper proposes a new thinking about applying AI methods, i.e., an AI method should be developed in such a way of assisting human controllers, but never in the way of competing and replacing human controllers. This new thinking is called human-machine coevolutionary intelligence (HMCEI). A methodological framework of HMCEI is further developed for decision-making support of ATM, in order to demonstrate the concept of HMCEI is practicably possible.

Original document

The different versions of the original document can be found in:

• https://hal-enac.archives-ouvertes.fr/hal-02972122

• http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9222913,

https://academic.microsoft.com/#/detail/3093822724

• http://xplorestaging.ieee.org/ielx7/9218469/9222855/09222913.pdf?arnumber=9222913,

http://dx.doi.org/10.1109/icns50378.2020.9222913